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首页> 外文期刊>The Journal of automatic chemistry >Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy
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Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy

机译：FT-NIR光谱法的快速地理原产地识别和天使中的Sinensis radix的质量评估

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摘要

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.
机译：Angelicae Sinensis Radix是一种广泛使用的中药和中国的香料。本研究的目的是制定Angelicae Sinensis Radix的地理分类方法，并使用近红外光谱测定样品中的阿魏酸和Z-锂钛的含量。建立了定性模型，以鉴定使用傅里叶变换近红外（FT-NIR）光谱法识别Angelicae Sinensis Radix的地理来源。支持向量机（SVM）算法用于建立定性模型。最佳SVM模型的校准率为校准集100％，预测集的83.72％。此外，建立了定量模型以预测使用FT-NIR来预测阿魏酸和Z-抗原的含量。局部最小二乘回归（PLSR）算法用于建立定量模型。 Synergy interval-PL（SI-PLS）用于筛选特征光谱间隔以获得最佳PLSR模型。用最佳光谱预处理方法建立的最佳PLSR模型的校准（R2C）的测定系数分别为约定的基酸和Z-LiGustilide的定量测定的最佳PLSR模型，分别为0.9659和0.9611，而确定系数预测（R2P）分别为0.9118和0.9206。两种最终优化PLSR模型的预测比率（RPD）的值大于2.结果表明NIR光谱与SVM和PLSR算法相结合，可以在不同地理位置的歧视中歧视Angelicae Sinensis Radix用于质量保证和监测。本研究可能是农业，制药和食品质量评估的参考。

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